[unreadable] [unreadable] In the three years since the original proposal was submitted, the claims we made about the impending readiness of knowledge-based approaches and natural language processing to address pressing problems of information overload in molecular biology have been resoundingly confirmed, and such methods have become increasingly accepted within the computational bioscience and systems biology communities. We are now well into the era of broad use of semantic representation technology to support biomedical research, and at the cusp of the use of biomedical natural language processing software to create the enormous number of necessary formal representations automatically from biomedical texts. The results of the work during the last funding period have not only contributed [unreadable] innovative and significant new methods, but have helped us identify a set of specific research issues we claim are now the rate-limiting factors in building an extensive, high-quality computational knowledge-base of molecular biology. The aims of this competitive renewal are to address those factors, making it possible to scale our impressive results on intentionally narrow applications to much [unreadable] larger (and more significant) tasks, specifically: (1) to create an enriched, relationally decomposed set of conceptual frames, hewing closely to multiple, community curated ontologies; (2) develop language processing tools capable of recognizing and populating instances of those conceptual frames, and (3) develop systems for integrating and using diverse knowledge from multiple sources to generate scientific insights, focusing on the analysis of sets of dozens to hundreds of genes produced by diverse high-throughput methodologies. An innovative aspect of this proposal is the creation and application of novel, insight-based extrinsic evaluation techniques for such systems. [unreadable] [unreadable] [unreadable]